These Jupyter notebooks supplement teaching of key model design concepts.

• Calibration: parameter space, objective function, ad-hoc calibration, gradient descent calibration.
• Uncertainty: observation error, parameter uncertainty, priors and posteriors, forecasts and prediction uncertainty, structural error.
• Wairakei: examples of the above as applied to the Wairakei geothermal system.

Download and unzip a local copy of this library. Open a Jupyter Notebook server and then open calibration/calibration.ipynb.

Alternatively, clone a copy of this library into your Microsoft Azure Account.

David Dempsey